Information Gravity: A Field-Theoretic Model for Token Selection in Large Language Models

We propose a theoretical model called "information gravity" to describe the text generation process in large language models (LLMs). The model uses physical apparatus from field theory and spacetime geometry to formalize the interaction between user queries and the probability distribution of generated tokens. A query is viewed as an object with "information mass" that curves the semantic space of the model, creating gravitational potential wells that "attract" tokens during generation. This model offers a mechanism to explain several observed phenomena in LLM behavior, including hallucinations (emerging from low-density semantic voids), sensitivity to query formulation (due to semantic field curvature changes), and the influence of sampling temperature on output diversity.